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Probabilistic movement models and zones of control

Article
Part of the following topical collections:
  1. Special Issue on Machine Learning for Soccer

Abstract

Coordinated movements of players are key to success in team sports. However, traditional models for player movements are based on unrealistic assumptions and their analysis is prone to errors. As a remedy, we propose to estimate individual movement models from positional data and show how to turn these estimates into accurate and realistic zones of control. Our approach accounts for characteristic traits of players, scales with large amounts of data, and can be efficiently computed in a distributed fashion. We report on empirical results.

Keywords

Positional data Movement models Zones of control Soccer 

Notes

Acknowledgements

The authors would like to thank Hendrik Weber and Deutsche Fußball Liga (DFL) and Sportcast GmbH for providing positional data.

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Leuphana UniversityLüneburgGermany
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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